A new unbiased stochastic derivative estimator for discontinuous sample performances with structural parameters

Yijie Peng, Michael C. Fu, Jian Qiang Hu, Bernd Heidergott

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Abstract

In this paper, we propose a new unbiased stochastic derivative estimator in a framework that can handle discontinuous sample performances with structural parameters. This work extends the three most popular unbiased stochastic derivative estimators: (1) infinitesimal perturbation analysis (IPA), (2) the likelihood ratio (LR) method, and (3) the weak derivative method, to a setting where they did not previously apply. Examples in probability constraints, control charts, and financial derivatives demonstrate the broad applicability of the proposed framework. The new estimator preserves the singlerun efficiency of the classic IPA-LR estimators in applications, which is substantiated by numerical experiments.

Original languageEnglish
Pages (from-to)487-499
Number of pages13
JournalOperations Research
Volume66
Issue number2
Early online date2 Feb 2018
DOIs
Publication statusPublished - Mar 2018

Funding

Funding:This work was supported in part by the National Natural Science Foundation of China [Grants 71571048, 71720107003, 71690232, 71371015], by the National Science Foundation [Grants CMMI-0856256, CMMI-1362303, CMMI-1434419], by the Air Force Office of Scientific Research [Grant FA9550-15-10050], and by the Science and Technology Agency of Sichuan Province [Grant 2014GZX0002].

FundersFunder number
Air Force Office of Scientific ResearchFA9550-15-10050
National Natural Science Foundation of China71720107003, 71371015, 71571048, 71690232
Department of Science and Technology of Sichuan Province2014GZX0002
National Science FoundationCMMI-1362303, CMMI-0856256, CMMI-1434419
National Aerospace Science Foundation of China

    Keywords

    • Discontinuous sample performance
    • Likelihood ratio
    • Perturbation analysis
    • Simulation
    • Stochastic derivative estimation
    • Weak derivative

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